The quality of images taken with long-range optical imaging systems can be severely degraded by atmospheric movements, such as turbulence and air movement, in the path between the region under observation and the imaging system. In particular, as distance increases, atmospheric turbulence is often the dominating source of image degradation in infrared and visible imaging applications. Assuming ideal observation conditions, the minimum distinguishable feature size that can be resolved using a given optical imaging system is bounded by the diffraction limit (1.22λ/D), where λ is the wavelength and D is the distance. This diffraction limit suggests that large-aperture optical imaging systems enable finer image features to be resolved/distinguished.
However, in large-aperture optical imaging systems, turbulence and air movement become the limiting factors long before the diffraction limit effects discussed above appear. In particular, the minimum distinguishable feature under turbulent conditions is given by the equation 1.22λ/R0, where R0 may be as small as a few centimeters and may be dependent on the strength of the turbulence and air movement. Thus, there is a practical limit on the ability to image distant objects in background art large-aperture optical imaging systems. Due to this, large-aperture optical imaging systems of the background art systems: (1) have not been able to take full advantage of the potential for increased resolution suggested by the diffraction limit; and (2) do not provide improvements in resolution and feature separation characteristics over smaller-aperture optical imaging systems.
For example, a very similar problem is faced by astronomers when trying to image the sky through the turbulent atmosphere of the earth using large telescopes. To overcome this limitation, special signal processing algorithms were developed that are capable of minimizing the effects of a turbulent atmospheric path by combining information from several images taken in a time sequence. A bispectral speckle imaging method described by C. J. Carrano, in “Speckle imaging over horizontal paths,” was presented at High Resolution Wavefront Control: Methods, Devices, and Applications IV, 2002. Unlike astronomical optical imaging, the challenges in large-aperture optical imaging systems for horizontal or slanted atmospheric paths are that the scenes are extended and the scene covers a very large visible angle. Thus, in general, the small-angle approximations typically used in astronomical applications cannot be directly applied to slanted path imaging systems.
In addition, the background art includes other digital signal processing techniques that have been applied to degraded images in an attempt to correct the images to overcome atmospheric turbulence. In an article by B. R. Frieden, entitled: “An exact, linear solution to the problem of imaging through turbulence,” Opt. Comm. 150 (1998) 15, a sequence of two short-exposure intensity images is taken without any reference point sources. The images are then Fourier transformed and divided by linear equations based on two random point spread functions. The result is then inverse filtered to provide an image of an object. However, a problem with this method is that the point spread functions associated with the turbulence are not known in an image due to the lack of any reference. This situation can cause further problems in recovering an image taken through turbulence. Other examples of background art in this technology area include, but are not limited to: U.S. Pat. No. 7,139,067S (Pohle et al.); U.S. Pat. No. 7,120,312 (George); U.S. patent application Ser. No. 10/661,138; U.S. patent application Ser. No. 11/017,384 (Olivier et al) and U.S. patent application Ser. No. 10/610,152 (Carrano et al).
As another example of the above-discussed background art (i.e., Carrano et al.), researchers at Lawrence Livermore National Laboratories have refined the astronomical bispectral speckle imaging methods and modified them for earth-based use. FIG. 1 is an exemplary block diagram 100 of this background art method. The method combines information from several images, taken a short time apart from one another. These can be a series of multiple short-exposure still shots from a conventional camera or, more commonly, a sequence of consecutive video frames. This information is combined and processed by complex “averaging” procedures in the frequency domain, where the magnitude and phase are calculated independently and subsequently recombined in the real space. However, on a personal computer (PC), this method requires several seconds to analyze a single frame. Thus, even though this bispectral method provides accurate results, it must be accelerated in order to work in real time.
To accommodate the spatially varying point spread functions experienced in earth-bound imaging, overlapping sub-fields of the image are separately speckle processed and re-assembled to form the full field of an the image. As shown in FIG. 2A and FIG. 2B, what results is a method that produces a single corrected image with quality near the diffraction limit. In FIG. 2A, the image frame represents original, degraded video image frame captures. FIG. 2B is the effect on the image frame after running the speckle imaging method on the degraded images. The computational rate required is a direct consequence of the large number of pixels in the image, which must be transformed into the frequency domain (e.g., by the Fast Fourier Transform (FFT)) and then to the bispectral domain. These transformations account for the majority of the computational time in the execution of the speckle algorithm.
If the above-discussed problems of the background art could be overcome, numerous applications could benefit from improvements in large-aperture, optical imaging. Most obvious are applications are the military field, particularly intelligence, reconnaissance, and target designation. Moreover, there are many civilian applications of this technology as well, especially in the surveillance and homeland security areas. Unfortunately, these atmospheric compensation algorithms are very computationally intensive, which prevents even top-of-the-line PCs from evaluating them in real time. The necessary processing typically requires tens of seconds to enhance a single frame. In addition, this duration of time for processing problem is worsened when video feeds are to be processed, since real-time video requires several dozen frames per second (e.g., a two order-of-magnitude difference). Therefore, there is a need in the art for improved computational methods and systems for large-aperture optical imaging systems that would allow real-time or increased performance.